CCSS: Quickest Detection Under Energy Constraints

CCSS：能量限制下最快的检测

基本信息

批准号：
1711468
负责人：
Lifeng Lai
金额：
$ 33万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-15 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711468&HistoricalAwards=false
关键词：
CCSS Quickest Detection Under Energy

项目摘要

Wireless sensor networks are commonly deployed to monitor abnormal changes in their surrounding environment. These changes typically imply certain activities of severe consequences, such as structure failure or chemical/gas leak, etc. Quickest detection is a framework that focuses on the design of sequential detection algorithms to identify such changes as quickly and reliably as possible so that we can win valuable time to take proper actions. In most of existing works, it is assumed that there is no constraint on how many and when sensors can take samples. This assumption may not hold for many applications in sensor networks whose sensors are powered either by battery with limited energy or renewable energy harvested from the environment. It is important to design novel quickest detection algorithms with energy constraints so that the designed algorithms can be used to detect abnormal activities using sensors powered by battery or renewable energy with a minimal delay. The energy constraints present significant challenges and unique features to quickest detection problems. It is crucial to design adaptive sensing strategies that rely on information extracted from samples taken so far and energy level at the battery to make sample and detection decisions. Towards this end, using tools from optimal stopping theory, the project aims to achieve the following goals: 1) to characterize the optimal detection schemes for problems with additional energy constraints; 2) to understand the performance loss associated with these energy constraints; and 3) to design low-complexity but asymptotically optimal detection schemes. To achieve these goals, the project will focus on two research thrusts. In the first research thrust, the project will focus on scenarios with a hard constraint on the total number of observations that the sensor is allowed to take. The designed algorithms in this thrust will be useful for sensors powered by battery with limited energy. In the second research thrust, the project will focus on scenarios with a stochastic energy constraint. The designed algorithms are suitable for sensors powered by renewable energy.

通常部署无线传感器网络以监视其周围环境中的异常变化。这些变化通常意味着某些严重后果的活动，例如结构故障或化学/气体泄漏等。最快的检测是一个框架，重点是设计顺序检测算法的设计，以尽可能快速，可靠地确定此类变化，以便我们可以赢得宝贵的时间来采取适当的行动。在大多数现有作品中，假定对传感器可以采集样品的数量和何时没有限制。对于传感器网络中的许多应用，这些假设可能无法持有，这些传感器网络中的传感器由电池提供有限的电池供电或从环境中收集的可再生能源供电。重要的是要设计具有能量限制的新型最快检测算法，以便可以使用由电池供电的传感器或可再生能源以最小的延迟来检测设计算法来检测异常活动。能量限制对最快的检测问题提出了重大挑战和独特的特征。设计自适应感测策略至关重要，这些传感策略依赖于迄今为止从迄今为止采集的样品中提取的信息以及电池的能量水平提取的信息来做出样本和检测决策。为此，使用最佳停止理论的工具，该项目旨在实现以下目标：1）表征具有其他能量限制的问题的最佳检测方案； 2）了解与这些能量限制相关的绩效损失； 3）设计低复杂性但渐近最佳检测方案。为了实现这些目标，该项目将重点放在两个研究推力上。在第一个研究作用中，该项目将重点放在方案上，并严格限制了允许传感器进行的观察总数。该推力中设计的算法对于由能量有限的电池供电的传感器将很有用。在第二次研究作用中，该项目将重点放在具有随机能量限制的场景上。设计的算法适用于由可再生能源提供动力的传感器。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Generalized Distributed Dual Coordinate Ascent in a Tree Network for Machine Learning

DOI：
10.1109/icassp.2019.8682185
发表时间：
2019-05
期刊：
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Myung Cho;L. Lai;Weiyu Xu
通讯作者：
Myung Cho;L. Lai;Weiyu Xu

Quickest Change-Point Detection Over Multiple Data Streams via Sequential Observations

DOI：
10.1109/icassp.2018.8461647
发表时间：
2018-04
期刊：
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Jun Geng;L. Lai
通讯作者：
Jun Geng;L. Lai

Privacy-Accuracy Trade-Off of Inference as Service